9,118 research outputs found
Performance evaluation of an open distributed platform for realistic traffic generation
Network researchers have dedicated a notable part of their efforts
to the area of modeling traffic and to the implementation of efficient traffic
generators. We feel that there is a strong demand for traffic generators
capable to reproduce realistic traffic patterns according to theoretical
models and at the same time with high performance. This work presents an open
distributed platform for traffic generation that we called distributed
internet traffic generator (D-ITG), capable of producing traffic (network,
transport and application layer) at packet level and of accurately replicating
appropriate stochastic processes for both inter departure time (IDT) and
packet size (PS) random variables. We implemented two different versions of
our distributed generator. In the first one, a log server is in charge of
recording the information transmitted by senders and receivers and these
communications are based either on TCP or UDP. In the other one, senders and
receivers make use of the MPI library. In this work a complete performance
comparison among the centralized version and the two distributed versions of
D-ITG is presented
Blocks adjustment -- reduction of bias and variance of detrended fluctuation analysis using Monte Carlo simulation
The length of minimal and maximal blocks equally distant on log-log scale
versus fluctuation function considerably influences bias and variance of DFA.
Through a number of extensive Monte Carlo simulations and different fractional
Brownian motion/fractional Gaussian noise generators, we found the pair of
minimal and maximal blocks that minimizes the sum of mean-squared error of
estimated Hurst exponents for the series of length N=2^p, p=7,...,15.
Sensitivity of DFA to sort-range correlations was examined using ARFIMA(p,d,q)
generator. Due to the bias of the estimator for anti-persistent processes, we
narrowed down the range of Hurst exponent to 1/2<=H< 1.Comment: 20 pages, 14 figures, accepted for publication in Physica A: August
9, 200
Internet's Critical Path Horizon
Internet is known to display a highly heterogeneous structure and complex
fluctuations in its traffic dynamics. Congestion seems to be an inevitable
result of user's behavior coupled to the network dynamics and it effects should
be minimized by choosing appropriate routing strategies. But what are the
requirements of routing depth in order to optimize the traffic flow? In this
paper we analyse the behavior of Internet traffic with a topologically
realistic spatial structure as described in a previous study (S-H. Yook et al.
,Proc. Natl Acad. Sci. USA, {\bf 99} (2002) 13382). The model involves
self-regulation of packet generation and different levels of routing depth. It
is shown that it reproduces the relevant key, statistical features of
Internet's traffic. Moreover, we also report the existence of a critical path
horizon defining a transition from low-efficient traffic to highly efficient
flow. This transition is actually a direct consequence of the web's small world
architecture exploited by the routing algorithm. Once routing tables reach the
network diameter, the traffic experiences a sudden transition from a
low-efficient to a highly-efficient behavior. It is conjectured that routing
policies might have spontaneously reached such a compromise in a distributed
manner. Internet would thus be operating close to such critical path horizon.Comment: 8 pages, 8 figures. To appear in European Journal of Physics B (2004
Fluid flow queue models for fixed-mobile network evaluation
A methodology for fast and accurate end-to-end KPI, like throughput and delay, estimation is proposed based on the service-centric traffic flow analysis and the fluid flow queuing model named CURSA-SQ. Mobile network features, like shared medium and mobility, are considered defining the models to be taken into account such as the propagation models and the fluid flow scheduling model. The developed methodology provides accurate computation of these KPIs, while performing orders of magnitude faster than discrete event simulators like ns-3. Finally, this methodology combined to its capacity for performance estimation in MPLS networks enables its application for near real-time converged fixed-mobile networks operation as it is proven in three use case scenarios
From source to target and back: symmetric bi-directional adaptive GAN
The effectiveness of generative adversarial approaches in producing images
according to a specific style or visual domain has recently opened new
directions to solve the unsupervised domain adaptation problem. It has been
shown that source labeled images can be modified to mimic target samples making
it possible to train directly a classifier in the target domain, despite the
original lack of annotated data. Inverse mappings from the target to the source
domain have also been evaluated but only passing through adapted feature
spaces, thus without new image generation. In this paper we propose to better
exploit the potential of generative adversarial networks for adaptation by
introducing a novel symmetric mapping among domains. We jointly optimize
bi-directional image transformations combining them with target self-labeling.
Moreover we define a new class consistency loss that aligns the generators in
the two directions imposing to conserve the class identity of an image passing
through both domain mappings. A detailed qualitative and quantitative analysis
of the reconstructed images confirm the power of our approach. By integrating
the two domain specific classifiers obtained with our bi-directional network we
exceed previous state-of-the-art unsupervised adaptation results on four
different benchmark datasets
Analysis of A Next Generation Energy System Based on the Integration of Transportation Subsystem Details
As the economy continues to grow, the current energy system will need to meet the increasing demand, especially in the developing countries. The depletion of fossil fuels, the surge in energy use, and the growing threat of climate change require rapid development of next-generation energy system. Renewable energy, such as wind, solar, and biomass, will undoubtedly play an important role, as a result of improved technology and enhanced capability in energy storage. For example, the closer integration of transportation to the energy system through vehicle electrification will have an increasing effect on the trajectory of the energy system. In order to gain a deeper understanding of the future energy system, anticipate potential problems during the evolution, and provide constructive suggestions for policy makers, a systematic analysis of the next generation energy system is highly desirable
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